A Method for Wafer Defect Detection Using Spatial Feature Points Guided Affine Iterative Closest Point Algorithm

Yang, Jing; Xu, Yi; Rong, Hai-Jun; Du, Shaoyi; Zhang, Hongmei

doi:10.1109/access.2020.2990535

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…Wafer surface defect detection has gone through three generations: (1) image processingbased algorithms that align the template image with the wafer image and highlight the defect area by difference operation [2][3][4][5]; (2) machine learning (ML)-based algorithms that utilize the machine learning algorithm to classify the defect area [6][7][8]; (3) deep learningbased algorithms that apply a deep convolutional neural network for classification and localization [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Zheng

Zhang

2023

Micromachines

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Concerning the problem that wafer surface defects are easily confused with the background and are difficult to detect, a new detection method for wafer surface defects based on background subtraction and Faster R-CNN is proposed. First, an improved spectral analysis method is proposed to measure the period of the image, and the substructure image can then be obtained on the basis of the period. Then, a local template matching method is adopted to position the substructure image, thereby reconstructing the background image. Then, the interference of the background can be eliminated by an image difference operation. Finally, the difference image is input into an improved Faster R-CNN network for detection. The proposed method has been validated on a self-developed wafer dataset and compared with other detectors. The experimental results show that compared with the original Faster R-CNN, the proposed method increases the mAP effectively by 5.2%, which can meet the requirements of intelligent manufacturing and high detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Zheng

Zhang

2023

Micromachines

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“…In the face of some irregularly shaped and weakly imaged defects, traditional algorithms suffer from low performance, high false detection rates, and high noise sensitivity. While computer vision is more effective in facing defects such as wafer stains, collapses, and cracks [6]. The use of computer vision will greatly save labor costs and is more suitable for highly integrated wafers.…”

Section: Introductionmentioning

confidence: 99%

Wafer Defect Detection Technology Based on CTM-IYOLOv5 Network

Yang,

Peng,

et al. 2023

Preprint

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With the development of semiconductor processing technology, the components within the wafers are highly integrated and complex, which can increase the number of surface defects. Traditional methods of wafer defect detection which were done manually by experienced engineers using computer-aided tools have some disadvantages, such as small defective area miss detection, low efficiency and tend to cause secondary contamination. Therefore, it is essential to identify dies defects quickly and locate defects timely, which can efficiently improve the yield of wafers. A novel wafer defect detection method based on clustering-template matching combined with improved YOLOv5 network (CTM-IYOLOv5) has been proposed in this paper. An inspection platform is used to acquire images of wafers in real-time. And the pre-processing operations are used for the preliminary processing of the acquired grain maps, which can reduce the computation of image processing and improve the efficiency of inspection. To solve the phenomenon of false detection and missed detection caused by multiple inter-grain channels in the field of view, clustering-template matching is used to segment the grains in the field of view. In addition, the convolutional layer structure, network structure, and data enhancement of YOLOv5 have been improved to become lighter, more accurate, and more suitable for small-target detection, with the model size been effectively reduced by 16%. The experimental results show that the accuracy of the proposed method is up to 99%, and the efficiency improvement of 44%, which can realize the need for real-time detection of wafer defects.

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“…Early wafer surface defect detection methods were mainly based on image processing technology [4][5][6][7]. In these methods, through the difference between a template image without defects and an image to be tested, each defect area is obtained using the threshold segmentation method, and the texture and shape features of the defect areas are extracted.…”

Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Feature Enhancement and Predicted Box Aggregation

Zheng

Dang²,

Zhang

2022

Electronics

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For wafer surface defect detection, a new method based on improved Faster RCNN is proposed here to solve the problems of missing detection due to small objects and multiple boxes detection due to discontinuous objects. First, focusing on the problem of small objects missing detection, a feature enhancement module (FEM) based on dynamic convolution is proposed to extract high-frequency image features, enrich the semantic information of shallow feature maps, and improve detection performance for small-scale defects. Second, for the multiple boxes detection caused by discontinuous objects, a predicted box aggregation method is proposed to aggregate redundant predicted boxes and fine-tune real predicted boxes to further improve positioning accuracy. Experimental results show that the mean average precision of the proposed method, when validated on a self-developed dataset, reached 87.5%, and the detection speed was 0.26 s per image. The proposed method has a certain engineering application value.

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A Method for Wafer Defect Detection Using Spatial Feature Points Guided Affine Iterative Closest Point Algorithm

Cited by 14 publications

References 32 publications

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Wafer Defect Detection Technology Based on CTM-IYOLOv5 Network

Wafer Surface Defect Detection Based on Feature Enhancement and Predicted Box Aggregation

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